ATF: Towards Robust Face Alignment via Leveraging Similarity and Diversity across Different Datasets

Abstract

Face alignment is an important task in the field of multi-media. Together with the impressive progress of algorithms, various benchmark datasets have been released in recent years. Intuitively, it is meaningful to integrate multiple labeled datasets with different annotations to achieve higher performance on a target landmark detector. Although numerous efforts have been made in joint usage, there yet remain three shortages in recent works, e.g., additional computation, limitation of the markups scheme, and limited support for the regression method. To address the above problems, we proposed a novel Alternating Training Framework (ATF), which leverages similarity and diversity across multi-media sources for a more robust detector. Our framework mainly contains two sub-modules: Alternating Training with Decreasing Proportions (ATDP) and Mixed Branch Loss (mathcal LMB). In particular, ATDP trains multiple datasets simultaneously to take advantage of the diversity between them, while mathcal LMB utilizes similar landmark pairs to constrain different branches of corresponding datasets. Extensive experiments on various benchmarks show the effectiveness of our framework, and ATF is feasible for both heatmap-based network and direct coordinate regression. Specifically, the mean error even reaches 3.17 on the experiment on 300W leveraging WFLW, which significantly outperforms state-of-the-art methods. Both in an ordinary convolutional network (OCN) and HRNET, ATF achieves up to 9.96% relative improvement. Our source codes are made publicly available at https://github.com/starhiking/ATF.